Overview

Dataset statistics

Number of variables23
Number of observations23446
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory4.8 MiB
Average record size in memory214.5 B

Variable types

Categorical1
Numeric22

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
chlorophyll is highly skewed (γ1 = 51.65583151)Skewed

Reproduction

Analysis started2023-02-02 21:39:21.926084
Analysis finished2023-02-02 21:39:53.800105
Duration31.87 seconds
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

siteid
Categorical

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size882.4 KiB
ARIK
 
1096
LECO
 
1096
WALK
 
1096
SYCA
 
1096
PRIN
 
1096
Other values (19)
17966 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters93784
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowARIK
2nd rowARIK
3rd rowARIK
4th rowARIK
5th rowARIK

Common Values

ValueCountFrequency (%)
ARIK 1096
 
4.7%
LECO 1096
 
4.7%
WALK 1096
 
4.7%
SYCA 1096
 
4.7%
PRIN 1096
 
4.7%
POSE 1096
 
4.7%
MCRA 1096
 
4.7%
MCDI 1096
 
4.7%
MAYF 1096
 
4.7%
LEWI 1096
 
4.7%
Other values (14) 12486
53.3%

Length

2023-02-02T16:39:53.840908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
arik 1096
 
4.7%
mayf 1096
 
4.7%
como 1096
 
4.7%
leco 1096
 
4.7%
king 1096
 
4.7%
wlou 1096
 
4.7%
lewi 1096
 
4.7%
hopb 1096
 
4.7%
mcdi 1096
 
4.7%
mcra 1096
 
4.7%
Other values (14) 12486
53.3%

Most occurring characters

ValueCountFrequency (%)
C 8547
 
9.1%
I 7767
 
8.3%
E 7644
 
8.2%
L 7012
 
7.5%
O 6968
 
7.4%
A 6922
 
7.4%
R 6837
 
7.3%
M 5451
 
5.8%
B 4692
 
5.0%
P 4247
 
4.5%
Other values (11) 27697
29.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 93784
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 8547
 
9.1%
I 7767
 
8.3%
E 7644
 
8.2%
L 7012
 
7.5%
O 6968
 
7.4%
A 6922
 
7.4%
R 6837
 
7.3%
M 5451
 
5.8%
B 4692
 
5.0%
P 4247
 
4.5%
Other values (11) 27697
29.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 93784
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 8547
 
9.1%
I 7767
 
8.3%
E 7644
 
8.2%
L 7012
 
7.5%
O 6968
 
7.4%
A 6922
 
7.4%
R 6837
 
7.3%
M 5451
 
5.8%
B 4692
 
5.0%
P 4247
 
4.5%
Other values (11) 27697
29.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 93784
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 8547
 
9.1%
I 7767
 
8.3%
E 7644
 
8.2%
L 7012
 
7.5%
O 6968
 
7.4%
A 6922
 
7.4%
R 6837
 
7.3%
M 5451
 
5.8%
B 4692
 
5.0%
P 4247
 
4.5%
Other values (11) 27697
29.5%

nitrate_mean
Real number (ℝ)

Distinct11109
Distinct (%)47.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.413339
Minimum0.001
Maximum851.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size882.4 KiB
2023-02-02T16:39:53.895203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.32083333
Q12.7708333
median6.1
Q321.471615
95-th percentile169.37135
Maximum851.7
Range851.699
Interquartile range (IQR)18.700781

Descriptive statistics

Standard deviation45.372312
Coefficient of variation (CV)2.0243441
Kurtosis14.711496
Mean22.413339
Median Absolute Deviation (MAD)4.7177083
Skewness3.4880584
Sum525503.15
Variance2058.6467
MonotonicityNot monotonic
2023-02-02T16:39:53.955217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.001 767
 
3.3%
3.3 33
 
0.1%
6.1 32
 
0.1%
3.55 32
 
0.1%
3.633333333 29
 
0.1%
3.65 29
 
0.1%
2.75 29
 
0.1%
3.14375 27
 
0.1%
4.55 26
 
0.1%
2.652083333 25
 
0.1%
Other values (11099) 22417
95.6%
ValueCountFrequency (%)
0.001 767
3.3%
0.001041666667 1
 
< 0.1%
0.002222222222 1
 
< 0.1%
0.003333333333 1
 
< 0.1%
0.007291666667 1
 
< 0.1%
0.008333333333 1
 
< 0.1%
0.00989010989 1
 
< 0.1%
0.01041666667 1
 
< 0.1%
0.01136363636 1
 
< 0.1%
0.01354166667 3
 
< 0.1%
ValueCountFrequency (%)
851.7 1
< 0.1%
493.6 1
< 0.1%
284.4541667 1
< 0.1%
264.3375 1
< 0.1%
261.76875 1
< 0.1%
261.3 1
< 0.1%
260.85625 1
< 0.1%
260.5569231 1
< 0.1%
259.7614583 1
< 0.1%
259.6916667 1
< 0.1%

specific_conductance
Real number (ℝ)

Distinct19471
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean583.87356
Minimum6.25 × 10-5
Maximum215585.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size882.4 KiB
2023-02-02T16:39:54.016212image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum6.25 × 10-5
5-th percentile13.48777
Q137.768763
median117.8826
Q3508.94584
95-th percentile685.11138
Maximum215585.2
Range215585.2
Interquartile range (IQR)471.17707

Descriptive statistics

Standard deviation6249.5379
Coefficient of variation (CV)10.703581
Kurtosis383.47817
Mean583.87356
Median Absolute Deviation (MAD)98.911444
Skewness19.25644
Sum13689500
Variance39056724
MonotonicityNot monotonic
2023-02-02T16:39:54.073564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
573.7750452 11
 
< 0.1%
34.77873521 11
 
< 0.1%
39.03122434 10
 
< 0.1%
37.93636806 10
 
< 0.1%
38.13544444 9
 
< 0.1%
466.7478697 9
 
< 0.1%
652.2436736 9
 
< 0.1%
38.02676389 9
 
< 0.1%
206.4707743 9
 
< 0.1%
111.3831285 9
 
< 0.1%
Other values (19461) 23350
99.6%
ValueCountFrequency (%)
6.25 × 10-51
< 0.1%
0.001541666667 1
< 0.1%
0.005796852077 1
< 0.1%
0.00871438499 1
< 0.1%
0.01110923771 1
< 0.1%
0.01912358986 1
< 0.1%
0.02366180362 1
< 0.1%
0.02713880685 1
< 0.1%
0.02773611111 1
< 0.1%
0.02871527778 1
< 0.1%
ValueCountFrequency (%)
215585.1969 1
 
< 0.1%
118086.5914 1
 
< 0.1%
118034.1458 1
 
< 0.1%
118032.4041 1
 
< 0.1%
118026.3421 1
 
< 0.1%
118017.1726 1
 
< 0.1%
118014.5285 1
 
< 0.1%
118010.4898 1
 
< 0.1%
118006.4988 6
< 0.1%
118001.9287 6
< 0.1%

DO
Real number (ℝ)

Distinct19409
Distinct (%)82.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.483089
Minimum0.001
Maximum354.97759
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size882.4 KiB
2023-02-02T16:39:54.136664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile4.6670184
Q18.2260217
median9.6660955
Q311.095766
95-th percentile13.169993
Maximum354.97759
Range354.97659
Interquartile range (IQR)2.869744

Descriptive statistics

Standard deviation16.81019
Coefficient of variation (CV)1.6035531
Kurtosis296.2088
Mean10.483089
Median Absolute Deviation (MAD)1.4369149
Skewness16.943946
Sum245786.5
Variance282.58248
MonotonicityNot monotonic
2023-02-02T16:39:54.194804image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.708430556 11
 
< 0.1%
12.08062847 10
 
< 0.1%
9.987256944 10
 
< 0.1%
8.924236111 9
 
< 0.1%
8.059923611 9
 
< 0.1%
9.389659722 9
 
< 0.1%
7.731243056 9
 
< 0.1%
4.162380764 9
 
< 0.1%
3.218755378 9
 
< 0.1%
8.680909722 9
 
< 0.1%
Other values (19399) 23352
99.6%
ValueCountFrequency (%)
0.001 3
< 0.1%
0.2234930556 1
 
< 0.1%
0.2289583333 1
 
< 0.1%
0.2346944444 1
 
< 0.1%
0.535875 1
 
< 0.1%
0.5426909722 1
 
< 0.1%
0.5442743056 1
 
< 0.1%
0.5455208333 1
 
< 0.1%
0.5476666667 1
 
< 0.1%
0.5570833333 1
 
< 0.1%
ValueCountFrequency (%)
354.9775903 1
 
< 0.1%
350.844588 1
 
< 0.1%
350.2903487 1
 
< 0.1%
348.9448664 5
< 0.1%
345.6860347 1
 
< 0.1%
344.8827361 1
 
< 0.1%
344.8493507 4
< 0.1%
343.2838435 1
 
< 0.1%
342.7693021 1
 
< 0.1%
339.5286148 1
 
< 0.1%

pH
Real number (ℝ)

Distinct19078
Distinct (%)81.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5137891
Minimum0.0025
Maximum13.790451
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size882.4 KiB
2023-02-02T16:39:54.254611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.0025
5-th percentile5.7870911
Q17.1478749
median7.6311563
Q37.9749115
95-th percentile8.3077426
Maximum13.790451
Range13.787951
Interquartile range (IQR)0.8270366

Descriptive statistics

Standard deviation0.87651541
Coefficient of variation (CV)0.11665425
Kurtosis12.338036
Mean7.5137891
Median Absolute Deviation (MAD)0.40040672
Skewness-0.19135221
Sum176168.3
Variance0.76827926
MonotonicityNot monotonic
2023-02-02T16:39:54.318217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.960267361 11
 
< 0.1%
7.9386875 10
 
< 0.1%
7.701305556 10
 
< 0.1%
8.078440972 10
 
< 0.1%
9.183684028 10
 
< 0.1%
6.944145833 10
 
< 0.1%
7.885038194 9
 
< 0.1%
7.507246528 9
 
< 0.1%
7.674159722 9
 
< 0.1%
7.505371528 9
 
< 0.1%
Other values (19068) 23349
99.6%
ValueCountFrequency (%)
0.0025 1
 
< 0.1%
0.02 1
 
< 0.1%
0.04 3
< 0.1%
0.04670833333 1
 
< 0.1%
0.06 2
< 0.1%
0.0805625 1
 
< 0.1%
0.09507638889 1
 
< 0.1%
0.09948611111 1
 
< 0.1%
0.1049166667 1
 
< 0.1%
0.1092361111 1
 
< 0.1%
ValueCountFrequency (%)
13.79045139 1
< 0.1%
13.68230208 1
< 0.1%
13.60482168 1
< 0.1%
13.50673628 1
< 0.1%
13.49504167 1
< 0.1%
13.47515278 1
< 0.1%
13.45948958 1
< 0.1%
13.41638647 1
< 0.1%
13.37937153 1
< 0.1%
13.36761084 1
< 0.1%

chlorophyll
Real number (ℝ)

Distinct18271
Distinct (%)77.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.532889
Minimum0.00092013889
Maximum59462.899
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size882.4 KiB
2023-02-02T16:39:54.382476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.00092013889
5-th percentile0.001
Q10.46566319
median1.2444427
Q34.2930165
95-th percentile102.89022
Maximum59462.899
Range59462.898
Interquartile range (IQR)3.8273533

Descriptive statistics

Standard deviation930.88464
Coefficient of variation (CV)23.547094
Kurtosis2866.5414
Mean39.532889
Median Absolute Deviation (MAD)1.043849
Skewness51.655832
Sum926888.11
Variance866546.21
MonotonicityNot monotonic
2023-02-02T16:39:54.440001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.001 1529
 
6.5%
2.829520833 10
 
< 0.1%
1.342516154 10
 
< 0.1%
0.7168263889 9
 
< 0.1%
5.652826389 9
 
< 0.1%
8.596649306 9
 
< 0.1%
0.7444097222 9
 
< 0.1%
29.24377083 8
 
< 0.1%
2.651560881 8
 
< 0.1%
1.154545139 8
 
< 0.1%
Other values (18261) 21837
93.1%
ValueCountFrequency (%)
0.0009201388889 1
 
< 0.1%
0.0009513888889 1
 
< 0.1%
0.001 1529
6.5%
0.001169544893 1
 
< 0.1%
0.001333984445 1
 
< 0.1%
0.001489583333 1
 
< 0.1%
0.001690751445 1
 
< 0.1%
0.001847222222 1
 
< 0.1%
0.002135416667 1
 
< 0.1%
0.002392361111 1
 
< 0.1%
ValueCountFrequency (%)
59462.8986 1
< 0.1%
58945.07041 1
< 0.1%
54365.8061 1
< 0.1%
53169.06921 1
< 0.1%
47121.93829 1
< 0.1%
35084.85759 1
< 0.1%
31630.10413 1
< 0.1%
27480.42869 1
< 0.1%
26948.5032 1
< 0.1%
25381.5971 1
< 0.1%

turbidity
Real number (ℝ)

Distinct14734
Distinct (%)62.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8006181
Minimum0.00013194444
Maximum99.988639
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size882.4 KiB
2023-02-02T16:39:54.595228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.00013194444
5-th percentile0.31123611
Q11.0980454
median2.683687
Q39.5134653
95-th percentile47.898694
Maximum99.988639
Range99.988507
Interquartile range (IQR)8.4154199

Descriptive statistics

Standard deviation16.964078
Coefficient of variation (CV)1.7309192
Kurtosis8.926077
Mean9.8006181
Median Absolute Deviation (MAD)2.1232321
Skewness2.910589
Sum229785.29
Variance287.77993
MonotonicityNot monotonic
2023-02-02T16:39:54.655368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.307083333 20
 
0.1%
11.51238194 19
 
0.1%
6.210263889 19
 
0.1%
78.25515278 19
 
0.1%
6.770125 18
 
0.1%
2.968525281 18
 
0.1%
51.03280556 18
 
0.1%
18.27320833 18
 
0.1%
9.185739583 18
 
0.1%
1.348585823 18
 
0.1%
Other values (14724) 23261
99.2%
ValueCountFrequency (%)
0.0001319444444 1
 
< 0.1%
0.0005158678381 1
 
< 0.1%
0.001 9
< 0.1%
0.001059459694 1
 
< 0.1%
0.001180555556 1
 
< 0.1%
0.001286269279 1
 
< 0.1%
0.002308008809 1
 
< 0.1%
0.003083333333 1
 
< 0.1%
0.003191978547 1
 
< 0.1%
0.003732638889 1
 
< 0.1%
ValueCountFrequency (%)
99.98863889 1
 
< 0.1%
99.89586806 1
 
< 0.1%
99.87997851 1
 
< 0.1%
99.85890278 1
 
< 0.1%
99.46018056 1
 
< 0.1%
99.44445139 6
< 0.1%
99.37908333 1
 
< 0.1%
99.35345486 1
 
< 0.1%
99.07136458 1
 
< 0.1%
98.91891262 1
 
< 0.1%

fDOM
Real number (ℝ)

Distinct16179
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.379228
Minimum0.0016666667
Maximum298.34492
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size882.4 KiB
2023-02-02T16:39:54.716558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.0016666667
5-th percentile1.184276
Q15.9634218
median15.809764
Q335.193319
95-th percentile88.596172
Maximum298.34492
Range298.34326
Interquartile range (IQR)29.229898

Descriptive statistics

Standard deviation34.655744
Coefficient of variation (CV)1.2657678
Kurtosis12.715532
Mean27.379228
Median Absolute Deviation (MAD)11.616319
Skewness3.0377977
Sum641933.38
Variance1201.0206
MonotonicityNot monotonic
2023-02-02T16:39:54.776848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.45 22
 
0.1%
45.22 19
 
0.1%
27.7235625 14
 
0.1%
1.601944444 14
 
0.1%
15.12207447 14
 
0.1%
25.38077778 14
 
0.1%
20.45387955 14
 
0.1%
109.2923125 13
 
0.1%
13.39816667 13
 
0.1%
0.4514305556 13
 
0.1%
Other values (16169) 23296
99.4%
ValueCountFrequency (%)
0.001666666667 1
 
< 0.1%
0.001807628524 1
 
< 0.1%
0.00725 1
 
< 0.1%
0.009034722222 1
 
< 0.1%
0.009215277778 1
 
< 0.1%
0.01093055556 1
 
< 0.1%
0.01320138889 11
< 0.1%
0.01429861111 1
 
< 0.1%
0.01854861111 1
 
< 0.1%
0.01926388889 1
 
< 0.1%
ValueCountFrequency (%)
298.3449236 1
 
< 0.1%
295.7704514 1
 
< 0.1%
295.0840134 1
 
< 0.1%
292.856303 1
 
< 0.1%
291.973625 9
< 0.1%
287.3111135 1
 
< 0.1%
284.1592708 1
 
< 0.1%
283.9141528 1
 
< 0.1%
283.8414861 1
 
< 0.1%
283.0132083 1
 
< 0.1%

mean_temp
Real number (ℝ)

Distinct17328
Distinct (%)73.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.89726
Minimum1.9013284
Maximum31.721088
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size882.4 KiB
2023-02-02T16:39:54.836853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.9013284
5-th percentile3.2524922
Q17.109186
median12.124423
Q318.377882
95-th percentile23.883193
Maximum31.721088
Range29.819759
Interquartile range (IQR)11.268696

Descriptive statistics

Standard deviation6.7680836
Coefficient of variation (CV)0.52476911
Kurtosis-1.0284933
Mean12.89726
Median Absolute Deviation (MAD)5.5913361
Skewness0.26025395
Sum302389.16
Variance45.806956
MonotonicityNot monotonic
2023-02-02T16:39:54.894361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.302480655 49
 
0.2%
4.348202381 12
 
0.1%
16.21949648 12
 
0.1%
3.839376488 12
 
0.1%
10.03405655 12
 
0.1%
21.83341964 12
 
0.1%
7.926578656 12
 
0.1%
9.31 12
 
0.1%
4.677092458 12
 
0.1%
11.32598958 12
 
0.1%
Other values (17318) 23289
99.3%
ValueCountFrequency (%)
1.901328383 1
 
< 0.1%
1.903630952 1
 
< 0.1%
1.911272321 1
 
< 0.1%
1.911690476 1
 
< 0.1%
1.911869048 1
 
< 0.1%
1.912936012 1
 
< 0.1%
1.913133929 1
 
< 0.1%
1.916729167 1
 
< 0.1%
1.917998512 6
< 0.1%
1.920572917 1
 
< 0.1%
ValueCountFrequency (%)
31.7210878 1
< 0.1%
31.24484673 1
< 0.1%
31.05685565 1
< 0.1%
31.03692708 1
< 0.1%
30.89431548 1
< 0.1%
30.84168155 1
< 0.1%
30.71561905 1
< 0.1%
30.71248214 1
< 0.1%
30.58959821 1
< 0.1%
30.57940774 1
< 0.1%

CH4_conc
Real number (ℝ)

Distinct733
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.588224
Minimum1.3109407
Maximum1121.0379
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size882.4 KiB
2023-02-02T16:39:54.954994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.3109407
5-th percentile1.7898706
Q12.2888922
median2.9568271
Q36.5429576
95-th percentile46.94904
Maximum1121.0379
Range1119.727
Interquartile range (IQR)4.2540654

Descriptive statistics

Standard deviation79.515231
Coefficient of variation (CV)4.7934748
Kurtosis143.0286
Mean16.588224
Median Absolute Deviation (MAD)0.9100072
Skewness11.210174
Sum388927.49
Variance6322.672
MonotonicityNot monotonic
2023-02-02T16:39:55.012370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.60143328 80
 
0.3%
3.71680165 80
 
0.3%
2.658842758 80
 
0.3%
2.512820683 70
 
0.3%
2.363340225 67
 
0.3%
1.7453277 55
 
0.2%
2.34971865 55
 
0.2%
1121.0379 54
 
0.2%
19.9759255 51
 
0.2%
1.74231325 51
 
0.2%
Other values (723) 22803
97.3%
ValueCountFrequency (%)
1.310940717 32
0.1%
1.34711825 33
0.1%
1.347839808 35
0.1%
1.36316455 31
0.1%
1.457755 30
0.1%
1.478376767 35
0.1%
1.517455375 39
0.2%
1.534133375 37
0.2%
1.56372325 43
0.2%
1.569164775 31
0.1%
ValueCountFrequency (%)
1121.0379 54
0.2%
1099.988239 38
0.2%
507.196405 31
0.1%
406.1683071 31
0.1%
359.0325085 31
0.1%
348.6181527 31
0.1%
327.7892796 31
0.1%
292.5923172 30
0.1%
278.9625211 30
0.1%
272.9822939 31
0.1%

CO2_conc
Real number (ℝ)

Distinct733
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1303.4592
Minimum451.65826
Maximum11310.682
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size882.4 KiB
2023-02-02T16:39:55.073345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum451.65826
5-th percentile607.96726
Q1740.02833
median925.39724
Q31308.5466
95-th percentile3981.2353
Maximum11310.682
Range10859.024
Interquartile range (IQR)568.51831

Descriptive statistics

Standard deviation1173.8768
Coefficient of variation (CV)0.90058574
Kurtosis17.864518
Mean1303.4592
Median Absolute Deviation (MAD)230.10609
Skewness3.7830308
Sum30560905
Variance1377986.7
MonotonicityNot monotonic
2023-02-02T16:39:55.133341image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1999.974753 80
 
0.3%
652.4161503 80
 
0.3%
921.5851002 80
 
0.3%
628.4179888 70
 
0.3%
675.6038126 67
 
0.3%
1030.656109 55
 
0.2%
726.9184898 55
 
0.2%
4111.138738 54
 
0.2%
1376.575813 51
 
0.2%
846.5851382 51
 
0.2%
Other values (723) 22803
97.3%
ValueCountFrequency (%)
451.658259 31
0.1%
468.3741605 30
0.1%
488.551533 33
0.1%
511.0961165 30
0.1%
517.5393612 36
0.2%
523.3695863 35
0.1%
530.5988639 31
0.1%
537.6745123 31
0.1%
538.5238653 30
0.1%
539.3033726 34
0.1%
ValueCountFrequency (%)
11310.68249 34
0.1%
8602.955041 29
0.1%
8470.422789 30
0.1%
7771.652797 32
0.1%
7545.073112 32
0.1%
7353.3547 40
0.2%
7223.292009 43
0.2%
6719.990008 30
0.1%
6232.460603 31
0.1%
6007.864268 31
0.1%

N2O_conc
Real number (ℝ)

Distinct733
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.64399083
Minimum0.22668198
Maximum17.283047
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size882.4 KiB
2023-02-02T16:39:55.194644image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.22668198
5-th percentile0.36388555
Q10.4552329
median0.55103137
Q30.68916245
95-th percentile1.0130376
Maximum17.283047
Range17.056366
Interquartile range (IQR)0.23392955

Descriptive statistics

Standard deviation0.77868384
Coefficient of variation (CV)1.2091536
Kurtosis320.71324
Mean0.64399083
Median Absolute Deviation (MAD)0.11167857
Skewness16.694549
Sum15099.009
Variance0.60634852
MonotonicityNot monotonic
2023-02-02T16:39:55.255309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9247087083 80
 
0.3%
0.721567075 80
 
0.3%
0.6338136583 80
 
0.3%
0.78556025 70
 
0.3%
0.62038335 67
 
0.3%
0.8191546 55
 
0.2%
0.5856849625 55
 
0.2%
0.761729675 54
 
0.2%
0.49425745 51
 
0.2%
0.4826633 51
 
0.2%
Other values (723) 22803
97.3%
ValueCountFrequency (%)
0.226681975 40
0.2%
0.2495983375 34
0.1%
0.2542558375 31
0.1%
0.2703872 8
 
< 0.1%
0.2727544 32
0.1%
0.2744453444 39
0.2%
0.2811430167 24
0.1%
0.2830501167 31
0.1%
0.28345535 30
0.1%
0.2839153167 23
0.1%
ValueCountFrequency (%)
17.2830475 31
0.1%
11.27185817 30
0.1%
6.1993433 31
0.1%
2.70986205 33
0.1%
2.41977485 30
0.1%
1.997072 33
0.1%
1.675499875 30
0.1%
1.54271025 31
0.1%
1.42676645 31
0.1%
1.384639 29
0.1%

Microbialabundanceper_ml
Real number (ℝ)

Distinct635
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2469686
Minimum4548.8889
Maximum62606310
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size882.4 KiB
2023-02-02T16:39:55.318716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4548.8889
5-th percentile46090
Q1162276.67
median328395.56
Q31115775.6
95-th percentile14852501
Maximum62606310
Range62601761
Interquartile range (IQR)953498.89

Descriptive statistics

Standard deviation6659992.6
Coefficient of variation (CV)2.6966961
Kurtosis32.50246
Mean2469686
Median Absolute Deviation (MAD)234294.44
Skewness5.0595365
Sum5.7904257 × 1010
Variance4.4355502 × 1013
MonotonicityNot monotonic
2023-02-02T16:39:55.380221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
295747.7778 193
 
0.8%
1281893.333 148
 
0.6%
165637.7778 139
 
0.6%
75857.77778 119
 
0.5%
332715.5556 110
 
0.5%
1223800 110
 
0.5%
593895.5556 102
 
0.4%
458984.4444 99
 
0.4%
320713.3333 95
 
0.4%
2596957.778 94
 
0.4%
Other values (625) 22237
94.8%
ValueCountFrequency (%)
4548.888889 37
0.2%
9002.222222 39
0.2%
9048.888889 35
0.1%
12447.77778 33
0.1%
12975.55556 44
0.2%
13443.33333 31
0.1%
14207.77778 43
0.2%
16585.55556 35
0.1%
17764.44444 31
0.1%
19081.11111 37
0.2%
ValueCountFrequency (%)
62606310 33
0.1%
61982167.78 31
0.1%
59389574.44 40
0.2%
39993141.11 33
0.1%
39849108.89 30
0.1%
39465021.11 32
0.1%
35361682.22 33
0.1%
32692272.22 33
0.1%
32397805.56 33
0.1%
30390946.67 31
0.1%

ln_nitrate_mean
Real number (ℝ)

Distinct11109
Distinct (%)47.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7430619
Minimum-6.9077553
Maximum6.7472344
Zeros3
Zeros (%)< 0.1%
Negative2270
Negative (%)9.7%
Memory size882.4 KiB
2023-02-02T16:39:55.440670image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-6.9077553
5-th percentile-1.1368335
Q11.0191481
median1.8082888
Q33.0667318
95-th percentile5.1320937
Maximum6.7472344
Range13.65499
Interquartile range (IQR)2.0475837

Descriptive statistics

Standard deviation2.1562068
Coefficient of variation (CV)1.2370225
Kurtosis6.294192
Mean1.7430619
Median Absolute Deviation (MAD)0.94863478
Skewness-1.9129667
Sum40867.83
Variance4.6492277
MonotonicityNot monotonic
2023-02-02T16:39:55.504170image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-6.907755279 767
 
3.3%
1.193922468 33
 
0.1%
1.808288771 32
 
0.1%
1.266947603 32
 
0.1%
1.290150501 29
 
0.1%
1.294727168 29
 
0.1%
1.011600912 29
 
0.1%
1.145416355 27
 
0.1%
1.515127233 26
 
0.1%
0.9753454947 25
 
0.1%
Other values (11099) 22417
95.6%
ValueCountFrequency (%)
-6.907755279 767
3.3%
-6.866933284 1
 
< 0.1%
-6.109247583 1
 
< 0.1%
-5.703782475 1
 
< 0.1%
-4.921023135 1
 
< 0.1%
-4.787491743 1
 
< 0.1%
-4.616220022 1
 
< 0.1%
-4.564348191 1
 
< 0.1%
-4.477336814 1
 
< 0.1%
-4.301983927 3
 
< 0.1%
ValueCountFrequency (%)
6.747234352 1
< 0.1%
6.201725473 1
< 0.1%
5.650572139 1
< 0.1%
5.577226696 1
< 0.1%
5.56746148 1
< 0.1%
5.565669173 1
< 0.1%
5.563969489 1
< 0.1%
5.562821352 1
< 0.1%
5.559763742 1
< 0.1%
5.55949503 1
< 0.1%

ln_specific_conductance
Real number (ℝ)

Distinct19471
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8163371
Minimum-9.680344
Maximum12.281111
Zeros0
Zeros (%)0.0%
Negative100
Negative (%)0.4%
Memory size882.4 KiB
2023-02-02T16:39:55.565057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-9.680344
5-th percentile2.6017834
Q13.6314824
median4.7696892
Q36.2323416
95-th percentile6.5295814
Maximum12.281111
Range21.961455
Interquartile range (IQR)2.6008592

Descriptive statistics

Standard deviation1.4392744
Coefficient of variation (CV)0.29883174
Kurtosis2.896539
Mean4.8163371
Median Absolute Deviation (MAD)1.2483955
Skewness-0.39019798
Sum112923.84
Variance2.0715107
MonotonicityNot monotonic
2023-02-02T16:39:55.713422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.352237412 11
 
< 0.1%
3.549006143 11
 
< 0.1%
3.66436195 10
 
< 0.1%
3.635910231 10
 
< 0.1%
3.64114415 9
 
< 0.1%
6.145789218 9
 
< 0.1%
6.480418225 9
 
< 0.1%
3.638290225 9
 
< 0.1%
5.330158874 9
 
< 0.1%
4.712975866 9
 
< 0.1%
Other values (19461) 23350
99.6%
ValueCountFrequency (%)
-9.680344001 1
< 0.1%
-6.474891197 1
< 0.1%
-5.150440254 1
< 0.1%
-4.742780172 1
< 0.1%
-4.49997829 1
< 0.1%
-3.956832635 1
< 0.1%
-3.743893192 1
< 0.1%
-3.606790588 1
< 0.1%
-3.585020065 1
< 0.1%
-3.550325971 1
< 0.1%
ValueCountFrequency (%)
12.28111146 1
 
< 0.1%
11.67917346 1
 
< 0.1%
11.67872923 1
 
< 0.1%
11.67871448 1
 
< 0.1%
11.67866312 1
 
< 0.1%
11.67858542 1
 
< 0.1%
11.67856302 1
 
< 0.1%
11.6785288 1
 
< 0.1%
11.67849498 6
< 0.1%
11.67845625 6
< 0.1%

ln_DO
Real number (ℝ)

Distinct19399
Distinct (%)82.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2117268
Minimum-6.9077553
Maximum5.8720547
Zeros0
Zeros (%)0.0%
Negative177
Negative (%)0.8%
Memory size882.4 KiB
2023-02-02T16:39:55.774276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-6.9077553
5-th percentile1.5405203
Q12.1073025
median2.2686245
Q32.4065636
95-th percentile2.577941
Maximum5.8720547
Range12.77981
Interquartile range (IQR)0.29926106

Descriptive statistics

Standard deviation0.44604047
Coefficient of variation (CV)0.20167069
Kurtosis43.485253
Mean2.2117268
Median Absolute Deviation (MAD)0.15100899
Skewness-1.7688056
Sum51856.147
Variance0.1989521
MonotonicityNot monotonic
2023-02-02T16:39:55.837281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.164291586 11
 
< 0.1%
2.491603217 10
 
< 0.1%
2.301309975 10
 
< 0.1%
2.188770734 9
 
< 0.1%
2.086904079 9
 
< 0.1%
2.239609054 9
 
< 0.1%
2.045269659 9
 
< 0.1%
1.42608721 9
 
< 0.1%
1.168994756 9
 
< 0.1%
2.16112633 9
 
< 0.1%
Other values (19389) 23352
99.6%
ValueCountFrequency (%)
-6.907755279 3
< 0.1%
-1.498374937 1
 
< 0.1%
-1.474215242 1
 
< 0.1%
-1.449470847 1
 
< 0.1%
-0.6238543541 1
 
< 0.1%
-0.611215233 1
 
< 0.1%
-0.6083019211 1
 
< 0.1%
-0.6060142832 1
 
< 0.1%
-0.6020884496 1
 
< 0.1%
-0.5850404392 1
 
< 0.1%
ValueCountFrequency (%)
5.872054662 1
 
< 0.1%
5.860343356 1
 
< 0.1%
5.858762378 1
 
< 0.1%
5.854913934 5
< 0.1%
5.845530949 1
 
< 0.1%
5.843204464 1
 
< 0.1%
5.843107657 4
< 0.1%
5.838557637 1
 
< 0.1%
5.837057632 1
 
< 0.1%
5.827558229 1
 
< 0.1%

ln_chlorophyll
Real number (ℝ)

Distinct18268
Distinct (%)77.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.16969534
Minimum-6.9909859
Maximum10.993108
Zeros0
Zeros (%)0.0%
Negative10239
Negative (%)43.7%
Memory size882.4 KiB
2023-02-02T16:39:55.898051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-6.9909859
5-th percentile-6.9077553
Q1-0.76429266
median0.2186878
Q31.4569896
95-th percentile4.6336626
Maximum10.993108
Range17.984094
Interquartile range (IQR)2.2212823

Descriptive statistics

Standard deviation2.6897845
Coefficient of variation (CV)15.850668
Kurtosis1.5113505
Mean0.16969534
Median Absolute Deviation (MAD)1.0927528
Skewness-0.64799879
Sum3978.6769
Variance7.2349405
MonotonicityNot monotonic
2023-02-02T16:39:55.958174image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-6.907755279 1529
 
6.5%
1.04010738 10
 
< 0.1%
0.2945455804 10
 
< 0.1%
-0.3329216031 9
 
< 0.1%
1.732155666 9
 
< 0.1%
2.151372512 9
 
< 0.1%
-0.2951636938 9
 
< 0.1%
3.375666588 8
 
< 0.1%
0.9751484784 8
 
< 0.1%
0.1437064473 8
 
< 0.1%
Other values (18258) 21837
93.1%
ValueCountFrequency (%)
-6.990985933 1
 
< 0.1%
-6.957587653 1
 
< 0.1%
-6.907755279 1529
6.5%
-6.751140587 1
 
< 0.1%
-6.619584992 1
 
< 0.1%
-6.50925884 1
 
< 0.1%
-6.382582207 1
 
< 0.1%
-6.29407227 1
 
< 0.1%
-6.149093491 1
 
< 0.1%
-6.035474488 1
 
< 0.1%
ValueCountFrequency (%)
10.99310784 1
< 0.1%
10.98436128 1
< 0.1%
10.90349067 1
< 0.1%
10.8812321 1
< 0.1%
10.76049395 1
< 0.1%
10.46552491 1
< 0.1%
10.36186461 1
< 0.1%
10.22122935 1
< 0.1%
10.20168303 1
< 0.1%
10.14177967 1
< 0.1%

ln_turbidity
Real number (ℝ)

Distinct14733
Distinct (%)62.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1703369
Minimum-8.9331296
Maximum4.6050566
Zeros0
Zeros (%)0.0%
Negative5187
Negative (%)22.1%
Memory size882.4 KiB
2023-02-02T16:39:56.022120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-8.9331296
5-th percentile-1.1672035
Q10.093531713
median0.98719159
Q32.2527082
95-th percentile3.8690882
Maximum4.6050566
Range13.538186
Interquartile range (IQR)2.1591765

Descriptive statistics

Standard deviation1.5484848
Coefficient of variation (CV)1.3231103
Kurtosis0.23039877
Mean1.1703369
Median Absolute Deviation (MAD)1.0427729
Skewness-0.02220011
Sum27439.718
Variance2.3978051
MonotonicityNot monotonic
2023-02-02T16:39:56.082617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.84167334 20
 
0.1%
2.443423147 19
 
0.1%
1.826203389 19
 
0.1%
4.359974677 19
 
0.1%
1.912519551 18
 
0.1%
1.088065291 18
 
0.1%
3.932468672 18
 
0.1%
2.905435962 18
 
0.1%
2.217652236 18
 
0.1%
0.2990565052 18
 
0.1%
Other values (14723) 23261
99.2%
ValueCountFrequency (%)
-8.933129599 1
 
< 0.1%
-7.569659953 1
 
< 0.1%
-6.907755279 9
< 0.1%
-6.849996223 1
 
< 0.1%
-6.741770142 1
 
< 0.1%
-6.656009283 1
 
< 0.1%
-6.071370114 1
 
< 0.1%
-5.781744016 1
 
< 0.1%
-5.74711432 1
 
< 0.1%
-5.590639819 1
 
< 0.1%
ValueCountFrequency (%)
4.605056568 1
 
< 0.1%
4.604128324 1
 
< 0.1%
4.60396925 1
 
< 0.1%
4.603758217 1
 
< 0.1%
4.599757369 1
 
< 0.1%
4.599599211 6
< 0.1%
4.598941662 1
 
< 0.1%
4.598683743 1
 
< 0.1%
4.595840445 1
 
< 0.1%
4.59430045 1
 
< 0.1%

ln_fDOM
Real number (ℝ)

Distinct16179
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6093324
Minimum-6.3969297
Maximum5.6982503
Zeros0
Zeros (%)0.0%
Negative994
Negative (%)4.2%
Memory size882.4 KiB
2023-02-02T16:39:56.143141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-6.3969297
5-th percentile0.16913165
Q11.7856444
median2.7606277
Q33.5608563
95-th percentile4.4840886
Maximum5.6982503
Range12.09518
Interquartile range (IQR)1.7752118

Descriptive statistics

Standard deviation1.3380573
Coefficient of variation (CV)0.51279682
Kurtosis1.1003332
Mean2.6093324
Median Absolute Deviation (MAD)0.86971242
Skewness-0.71124636
Sum61178.407
Variance1.7903974
MonotonicityNot monotonic
2023-02-02T16:39:56.206671image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.737609003 22
 
0.1%
3.811539467 19
 
0.1%
3.322282683 14
 
0.1%
0.4712181692 14
 
0.1%
2.716155562 14
 
0.1%
3.233992107 14
 
0.1%
3.018172574 14
 
0.1%
4.694026059 13
 
0.1%
2.595117882 13
 
0.1%
-0.7953337262 13
 
0.1%
Other values (16169) 23296
99.4%
ValueCountFrequency (%)
-6.396929655 1
 
< 0.1%
-6.3157395 1
 
< 0.1%
-4.92675381 1
 
< 0.1%
-4.7066801 1
 
< 0.1%
-4.686892544 1
 
< 0.1%
-4.51619315 1
 
< 0.1%
-4.327433236 11
< 0.1%
-4.247592872 1
 
< 0.1%
-3.987360365 1
 
< 0.1%
-3.949522978 1
 
< 0.1%
ValueCountFrequency (%)
5.698250279 1
 
< 0.1%
5.689583651 1
 
< 0.1%
5.687260107 1
 
< 0.1%
5.679682055 1
 
< 0.1%
5.676663473 9
< 0.1%
5.660565648 1
 
< 0.1%
5.649534894 1
 
< 0.1%
5.648671914 1
 
< 0.1%
5.648415935 1
 
< 0.1%
5.645493569 1
 
< 0.1%

ln_CH4_conc
Real number (ℝ)

Distinct733
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5246
Minimum0.27074498
Maximum7.0220102
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size882.4 KiB
2023-02-02T16:39:56.267053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.27074498
5-th percentile0.5821433
Q10.82806792
median1.0841168
Q31.8783893
95-th percentile3.8490628
Maximum7.0220102
Range6.7512652
Interquartile range (IQR)1.0503214

Descriptive statistics

Standard deviation1.0976274
Coefficient of variation (CV)0.71994449
Kurtosis4.5480744
Mean1.5246
Median Absolute Deviation (MAD)0.33362711
Skewness2.0077704
Sum35745.773
Variance1.2047859
MonotonicityNot monotonic
2023-02-02T16:39:56.327180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.681119694 80
 
0.3%
1.312863527 80
 
0.3%
0.9778909749 80
 
0.3%
0.9214059004 70
 
0.3%
0.860075968 67
 
0.3%
0.5569423317 55
 
0.2%
0.8542955976 55
 
0.2%
7.022010231 54
 
0.2%
2.994527823 51
 
0.2%
0.5552136843 51
 
0.2%
Other values (723) 22803
97.3%
ValueCountFrequency (%)
0.2707449838 32
0.1%
0.2979676813 33
0.1%
0.2985031689 35
0.1%
0.3098088718 31
0.1%
0.3768975811 30
0.1%
0.3909447066 35
0.1%
0.4170348366 39
0.2%
0.4279656451 37
0.2%
0.4470696763 43
0.2%
0.4505434874 31
0.1%
ValueCountFrequency (%)
7.022010231 54
0.2%
7.003054767 38
0.2%
6.228898315 31
0.1%
6.006767623 31
0.1%
5.883412937 31
0.1%
5.853977205 31
0.1%
5.792370962 31
0.1%
5.678780231 30
0.1%
5.63107744 30
0.1%
5.609406936 31
0.1%

ln_CO2_conc
Real number (ℝ)

Distinct733
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9772466
Minimum6.1129258
Maximum9.3335029
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size882.4 KiB
2023-02-02T16:39:56.402903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum6.1129258
5-th percentile6.410121
Q16.6066885
median6.8302231
Q37.1766724
95-th percentile8.2893474
Maximum9.3335029
Range3.2205771
Interquartile range (IQR)0.56998389

Descriptive statistics

Standard deviation0.54009606
Coefficient of variation (CV)0.077408194
Kurtosis2.5768079
Mean6.9772466
Median Absolute Deviation (MAD)0.26164133
Skewness1.5764484
Sum163588.52
Variance0.29170375
MonotonicityNot monotonic
2023-02-02T16:39:56.476242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.600889836 80
 
0.3%
6.480682626 80
 
0.3%
6.826095122 80
 
0.3%
6.443205532 70
 
0.3%
6.515606828 67
 
0.3%
6.937950877 55
 
0.2%
6.588814353 55
 
0.2%
8.321455334 54
 
0.2%
7.227354399 51
 
0.2%
6.741210773 51
 
0.2%
Other values (723) 22803
97.3%
ValueCountFrequency (%)
6.11292583 31
0.1%
6.149267465 30
0.1%
6.191444958 33
0.1%
6.236557667 30
0.1%
6.249085583 36
0.2%
6.26028788 35
0.1%
6.2740063 31
0.1%
6.287253381 31
0.1%
6.288831814 30
0.1%
6.290278256 34
0.1%
ValueCountFrequency (%)
9.333502911 34
0.1%
9.059861033 29
0.1%
9.044335702 30
0.1%
8.958238136 32
0.1%
8.928650061 32
0.1%
8.90291191 40
0.2%
8.885066085 43
0.2%
8.812841947 30
0.1%
8.737526494 31
0.1%
8.700824601 31
0.1%

ln_N2O_conc
Real number (ℝ)

Distinct733
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.55452449
Minimum-1.4842072
Maximum2.8497261
Zeros0
Zeros (%)0.0%
Negative22075
Negative (%)94.2%
Memory size882.4 KiB
2023-02-02T16:39:56.555107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-1.4842072
5-th percentile-1.0109159
Q1-0.78694612
median-0.59596354
Q3-0.37227826
95-th percentile0.012953367
Maximum2.8497261
Range4.3339333
Interquartile range (IQR)0.41466786

Descriptive statistics

Standard deviation0.37247426
Coefficient of variation (CV)-0.6717003
Kurtosis16.034113
Mean-0.55452449
Median Absolute Deviation (MAD)0.20277513
Skewness2.3476445
Sum-13001.381
Variance0.13873708
MonotonicityNot monotonic
2023-02-02T16:39:56.631054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.07827650097 80
 
0.3%
-0.326329939 80
 
0.3%
-0.456000282 80
 
0.3%
-0.2413581215 70
 
0.3%
-0.4774176856 67
 
0.3%
-0.1994824462 55
 
0.2%
-0.5349732406 55
 
0.2%
-0.2721635435 54
 
0.2%
-0.7046987437 51
 
0.2%
-0.7284359699 51
 
0.2%
Other values (723) 22803
97.3%
ValueCountFrequency (%)
-1.484207235 40
0.2%
-1.387902303 34
0.1%
-1.369414285 31
0.1%
-1.307900273 8
 
< 0.1%
-1.299183522 32
0.1%
-1.293003147 39
0.2%
-1.268891783 24
0.1%
-1.262131306 31
0.1%
-1.260700664 30
0.1%
-1.259079266 23
0.1%
ValueCountFrequency (%)
2.849726108 31
0.1%
2.422309192 30
0.1%
1.824443367 31
0.1%
0.9968977295 33
0.1%
0.8836744987 30
0.1%
0.6916821079 33
0.1%
0.5161115536 30
0.1%
0.4335407722 31
0.1%
0.3554106601 31
0.1%
0.3254394558 29
0.1%
Distinct629
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.049861
Minimum8.4226383
Maximum17.952377
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size882.4 KiB
2023-02-02T16:39:56.703493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum8.4226383
5-th percentile10.738351
Q111.997058
median12.701974
Q313.92506
95-th percentile16.513676
Maximum17.952377
Range9.5297383
Interquartile range (IQR)1.9280023

Descriptive statistics

Standard deviation1.6867758
Coefficient of variation (CV)0.12925623
Kurtosis0.19764014
Mean13.049861
Median Absolute Deviation (MAD)0.91862529
Skewness0.63191124
Sum305967.04
Variance2.8452125
MonotonicityNot monotonic
2023-02-02T16:39:56.770012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.59726227 193
 
0.8%
14.06384871 148
 
0.6%
12.01755862 139
 
0.6%
11.23661552 119
 
0.5%
12.16797841 117
 
0.5%
14.01747133 110
 
0.5%
12.71504322 110
 
0.5%
13.29445875 102
 
0.4%
13.0367716 99
 
0.4%
12.67830296 95
 
0.4%
Other values (619) 22214
94.7%
ValueCountFrequency (%)
8.422638282 37
0.2%
9.105226739 39
0.2%
9.110397254 35
0.1%
9.429297394 33
0.1%
9.470822525 44
0.2%
9.506238599 31
0.1%
9.561544824 43
0.2%
9.716287448 35
0.1%
9.784954235 31
0.1%
9.856454178 37
0.2%
ValueCountFrequency (%)
17.95237663 33
0.1%
17.94235729 31
0.1%
17.89962925 40
0.2%
17.50421853 33
0.1%
17.5006106 30
0.1%
17.4909253 32
0.1%
17.38113937 33
0.1%
17.30264928 33
0.1%
17.29360125 33
0.1%
17.22965532 31
0.1%

Interactions

2023-02-02T16:39:52.018761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:22.308925image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:23.712237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:25.057219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:26.513117image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:27.997838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:29.431175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:30.912959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:32.274894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:33.676343image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:35.074672image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:36.500092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:37.857488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:39.322668image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:40.759309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:42.177143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:43.499290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:45.015567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:46.427757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:47.856366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:49.331458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:50.658541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:52.076984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:22.362921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:23.772221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:25.219867image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:26.570113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:28.055587image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:29.502816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:30.968663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:32.331312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:33.732522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:35.142659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:36.559781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:37.915586image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:39.381814image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:40.813780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:42.235097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:43.559010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:45.077355image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:46.485780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:47.912446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:49.389551image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:50.712638image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:52.136961image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:22.423118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:23.826464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:25.277334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:26.627323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:28.116130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:29.575350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:31.028573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:32.389474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:33.790528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:35.206770image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:36.619929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:37.979957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:39.445976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:40.872959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:42.295757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:43.623724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:45.137479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:46.543835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:47.972155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:49.447303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:50.770247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:52.199858image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:22.483334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:23.887971image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:25.340533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:26.690394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:28.176104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:29.645293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:31.088608image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:32.449289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:33.849115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2023-02-02T16:39:42.112970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:43.438742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:44.940810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:46.365514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:47.797353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:49.179988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:50.595393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:51.960276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Missing values

2023-02-02T16:39:53.512916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-02T16:39:53.694312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

siteidnitrate_meanspecific_conductanceDOpHchlorophyllturbidityfDOMmean_tempCH4_concCO2_concN2O_concMicrobialabundanceper_mlln_nitrate_meanln_specific_conductanceln_DOln_chlorophyllln_turbidityln_fDOMln_CH4_concln_CO2_concln_N2O_concln_Microbialabundanceper_ml
date
2018-01-01ARIK13.700000539.05944410.1863897.8225002.0950002.89944445.2200002.30248125.7928471621.7227010.49118652674.4444442.6173966.2898262.3210520.7395541.0645193.8115393.2500977.391244-0.71093310.871886
2018-01-02ARIK13.700000539.05944410.1863897.8225002.0950002.89944445.2200002.30248125.7928471621.7227010.49118652674.4444442.6173966.2898262.3210520.7395541.0645193.8115393.2500977.391244-0.71093310.871886
2018-01-03ARIK13.700000539.05944410.1863897.8225002.0950002.89944445.2200002.30248125.7928471621.7227010.49118652674.4444442.6173966.2898262.3210520.7395541.0645193.8115393.2500977.391244-0.71093310.871886
2018-01-04ARIK12.535417546.4800429.9217857.8124033.24946560.36984045.2200002.30248125.7928471621.7227010.49118652674.4444442.5285586.3034982.2947331.1784904.1004903.8115393.2500977.391244-0.71093310.871886
2018-01-05ARIK10.310417550.8302089.7217297.8267435.5652362.54800745.2200002.30248125.7928471621.7227010.49118652674.4444442.3331556.3114272.2743641.7165390.9353113.8115393.2500977.391244-0.71093310.871886
2018-01-06ARIK9.966667535.34356910.0605287.8392228.42205633.21916745.2200002.30248125.7928471621.7227010.49118652674.4444442.2992466.2829092.3086202.1308543.5031273.8115393.2500977.391244-0.71093310.871886
2018-01-07ARIK9.207292503.49446510.3337017.8834799.2041673.41703545.2200002.30248125.7928471621.7227010.49118652674.4444442.2199966.2215732.3354112.2196561.2287733.8115393.2500977.391244-0.71093310.871886
2018-01-08ARIK8.103125486.38858210.6450007.90935412.09032619.39932645.2200002.30248125.7928471621.7227010.49118652674.4444442.0922506.1870082.3650902.4924062.9652383.8115393.2500977.391244-0.71093310.871886
2018-01-09ARIK7.708333475.40259210.6507157.92991043.9297364.27802817.5900002.30248125.7928471621.7227010.49118652674.4444442.0423026.1641622.3656273.7825911.4534922.8673313.2500977.391244-0.71093310.871886
2018-01-10ARIK6.932292467.58849310.5777437.94794430.26864611.8849100.4514312.30248125.7928471621.7227010.49118652674.4444441.9361906.1475892.3587523.4101122.475270-0.7953343.2500977.391244-0.71093310.871886
siteidnitrate_meanspecific_conductanceDOpHchlorophyllturbidityfDOMmean_tempCH4_concCO2_concN2O_concMicrobialabundanceper_mlln_nitrate_meanln_specific_conductanceln_DOln_chlorophyllln_turbidityln_fDOMln_CH4_concln_CO2_concln_N2O_concln_Microbialabundanceper_ml
date
2020-12-22WLOU3.808333110.89398310.4145078.0930031.3441671.8892784.58103515.1607532.807956805.8041450.72599554252.2222221.3371924.7085752.3432000.2957740.6361951.5219251.0324576.691841-0.32021210.901399
2020-12-23WLOU3.822917111.12055610.4083098.1041460.9894414.4692334.63829919.0594262.807956805.8041450.72599554252.2222221.3410144.7106162.342604-0.0106151.4972171.5343481.0324576.691841-0.32021210.901399
2020-12-24WLOU3.650000111.48186910.4959768.1035000.9282382.3897504.5185007.9265792.807956805.8041450.72599554252.2222221.2947274.7138622.350992-0.0744670.8711891.5081801.0324576.691841-0.32021210.901399
2020-12-25WLOU54.90208368.81119910.6192087.3442992.6619200.34067427.6552644.4804902.807956805.8041450.72599554252.2222224.0055514.2313672.3626640.979048-1.0768303.3198161.0324576.691841-0.32021210.901399
2020-12-26WLOU27.848958630.31836811.7336117.8204930.2548090.7221018.41608121.2526032.807956805.8041450.72599554252.2222223.3267966.4462252.462457-1.367241-0.3255912.1301441.0324576.691841-0.32021210.901399
2020-12-27WLOU0.001000298.29189211.8997408.1460280.2200420.95909716.21523617.3897862.807956805.8041450.72599554252.222222-6.9077555.6980732.476517-1.513938-0.0417632.7859511.0324576.691841-0.32021210.901399
2020-12-28WLOU0.04175812.1051468.3842536.6516260.9006842.63997631.65502810.1731462.807956805.8041450.72599554252.222222-3.1758582.4936312.126355-0.1046010.9707703.4548971.0324576.691841-0.32021210.901399
2020-12-29WLOU4.188542613.85351210.9006087.8691089.8175000.6004204.0829174.3258742.807956805.8041450.72599554252.2222221.4323536.4197562.3888192.284167-0.5101261.4068121.0324576.691841-0.32021210.901399
2020-12-30WLOU54.432292176.73539914.5619517.9488061.6422812.160620142.7745833.8393762.807956805.8041450.72599554252.2222223.9969585.1746542.6784120.4960860.7703954.9612671.0324576.691841-0.32021210.901399
2020-12-31WLOU54.08020811.79975712.4422927.61187210.6312530.66855254.2513338.3642472.807956805.8041450.72599554252.2222223.9904682.4680792.5211012.363798-0.4026413.9936281.0324576.691841-0.32021210.901399

Duplicate rows

Most frequently occurring

siteidnitrate_meanspecific_conductanceDOpHchlorophyllturbidityfDOMmean_tempCH4_concCO2_concN2O_concMicrobialabundanceper_mlln_nitrate_meanln_specific_conductanceln_DOln_chlorophyllln_turbidityln_fDOMln_CH4_concln_CO2_concln_N2O_concln_Microbialabundanceper_ml# duplicates
0ARIK13.7539.05944410.1863897.82252.0952.89944445.222.30248125.7928471621.7227010.49118652674.4444442.6173966.2898262.3210520.7395541.0645193.8115393.2500977.391244-0.71093310.8718863